2 results
2235: Equipping health professional students to apply pharmacogenomic data to clinical decision making in real-world scenarios: Comparison of an active engagement Versus didactic teaching approach
- Meghan J. Arwood, Caitrin Rowe McDonough, Larisa H. Cavallari, Amanda R. Elsey, Reginald F. Frye, Yan Gong, Julie A. Johnson, Kristin W. Weitzel, Taimour Langaee
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- Journal:
- Journal of Clinical and Translational Science / Volume 1 / Issue S1 / September 2017
- Published online by Cambridge University Press:
- 10 May 2018, p. 46
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OBJECTIVES/SPECIFIC AIMS: Compare effectiveness of a patient case-based, interactive teaching approach that included optional student genotyping with traditional didactic teaching strategies for increasing students’ knowledge and ability to effectively use pharmacogenomic data in clinical decision making. METHODS/STUDY POPULATION: The UF College of Pharmacy offers a required Personalized Medicine (PM) course for pharmacy students as well as an elective course, Clinical Applications of Personalized Medicine (CAPM). Students dual enrolled in the PM and elective CAPM courses comprised the intervention (INT) group, with interactive patient case-based teaching and the option to undergo personal genotyping, whereas students enrolled in PM alone comprised the control (CTR) group, which primarily used a traditional didactic teaching format and did not include personal genotyping. Both groups completed a pre- and post-course patient case-based test (15 questions/1 point each) to evaluate their knowledge and abilities to apply genotype and other patient-specific data to drug therapy recommendations. Pre- and post-course test scores for knowledge were compared between the INT and CTR groups using the Student t-test. RESULTS/ANTICIPATED RESULTS: In total, 52 students completed surveys (INT group, n=21; CTR group, n=31). Race was similar between groups, but there were fewer females in the INT compared with CTR group (8 vs. 22, p=0.02). Pre-course knowledge scores did not differ between INT and CTR groups (6.8±2.2 vs. 6.3±1.6 respectively, p=0.34), however, post-course scores were significantly higher in the INT Versus CTR group (10.0±2.3 vs. 7.5±1.7, p<0.0001). DISCUSSION/SIGNIFICANCE OF IMPACT: There have been significant advancements in the clinical applications of pharmacogenomic and genomic data, however, barriers to routine clinical adoption of genomic medicine persist. Developing education and training methods that equip practitioners to effectively translate genomic data into evidence-based clinical recommendations has been identified as a key strategy to overcome such barriers. Our data suggest that a personalized medicine course that employs patient-centered, case-based teaching strategies and includes optional personal genotyping for students compared with traditional didactic instruction improves students’ knowledge and abilities to apply pharmacogenomic data in practice-based scenarios. These results can inform future strategies for educating healthcare professionals on the clinical use of pharmacogenomic and genomic data.
2246: Characterization of resistant hypertension in a statewide electronic health record-based database (OneFlorida)
- Caitrin W. McDonough, William R. Hogan, Betsy Shenkman, Rhonda M. Cooper-DeHoff
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- Journal:
- Journal of Clinical and Translational Science / Volume 1 / Issue S1 / September 2017
- Published online by Cambridge University Press:
- 10 May 2018, p. 13
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OBJECTIVES/SPECIFIC AIMS: Our objective is to create a Resistant Hypertension (RHTN) computable phenotype from electronic health record (EHR)-based data, and to determine the characteristics associated with RHTN within a large, diverse, EHR-based database. METHODS/STUDY POPULATION: The OneFlorida Clinical Research Consortium includes 10 unique health care systems providing care for approximately half of the state (48%, ~10 million). OneFlorida houses a Data Trust which contains longitudinal EHR data and claims data from these providers in a common format, the PCORnet common data model v3.0. For the current project, data from 5 health care systems were considered. All of the adult hypertension (HTN) patients with a HTN diagnosis from an outpatient encounter were extracted from the OneFlorida Data Trust. Additional data such as demographics, prescribing, and vitals information were also extracted. The RHTN computable phenotype was created by constructing a drug exposure variable that took into consideration the number of antihypertensive medications an individual was prescribed at any point in time over the course of the OneFlorida dataset. RHTN was defined as any blood pressure requiring four or more antihypertensive drugs, or uncontrolled blood pressure (≥140/90) on 3 antihypertensive drugs. RHTN cases had to meet the definition criteria twice during the data period, at least 30 days apart. All data extraction, computation phenotype coding, and statistical analyses were conducted using SQL or SAS. RESULTS/ANTICIPATED RESULTS: Our preliminary results show that there were n=342,026 adults with a HTN diagnosis from an outpatient visit in the data set. After the RHTN computable phenotype was constructed, n=11,670 RHTN cases were identified from the n=130,901 HTN individuals with all of the required variables in the data set (8.9% RHTN prevalence). In all, 55% of RHTN cases were Black or African American, compared with the total HTN population (25% Black/African American). RHTN cases also had more prescriptions for loop diuretics, centrally acting agents, α-blockers, and vasodilators compared with the total HTN population. Not surprisingly, the RHTN cases had 26% of the antihypertensive prescriptions in the data set, and the RHTN cases had fewer blood pressure readings that were in control (only 49.4% of readings <140/90). DISCUSSION/SIGNIFICANCE OF IMPACT: Overall, our preliminary data shows that it is possible to create the very complicated computable phenotype of RHTN within the OneFlorida Data Trust. We found that the RHTN prevalence in OneFlorida is 8.9% which is consistent with previous studies from NHANES. Although promising, these results require further validation of the computable phenotype and replication in other similar data sets in order to ascertain their true meaning. Once validated, the experience gained from this computable phenotype can be applied to many other phenotypes.